{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c0e1c33",
   "metadata": {},
   "source": [
    "# data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b229b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import os \n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "data = pd.read_csv(\"month.csv\")\n",
    "customer = pd.read_csv(\"country.csv\")\n",
    "rr = pd.read_csv(\"rr.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2e96855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>cancel_invoice</th>\n",
       "      <th>GMV</th>\n",
       "      <th>first_purchase</th>\n",
       "      <th>cohort_index</th>\n",
       "      <th>cohort_month</th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>invoice_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>83.4</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>200912</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>200912</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>200912</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>100.8</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>200912</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>0</td>\n",
       "      <td>200912</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Invoice StockCode                          Description  \\\n",
       "0           0   489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS   \n",
       "1           1   489434    79323P                   PINK CHERRY LIGHTS   \n",
       "2           2   489434    79323W                  WHITE CHERRY LIGHTS   \n",
       "3           3   489434     22041         RECORD FRAME 7\" SINGLE SIZE    \n",
       "4           4   489434     21232       STRAWBERRY CERAMIC TRINKET BOX   \n",
       "\n",
       "   Quantity     InvoiceDate  Price  Customer ID         Country        Date  \\\n",
       "0        12  2009/12/1 7:45   6.95        13085  United Kingdom  2009-12-01   \n",
       "1        12  2009/12/1 7:45   6.75        13085  United Kingdom  2009-12-01   \n",
       "2        12  2009/12/1 7:45   6.75        13085  United Kingdom  2009-12-01   \n",
       "3        48  2009/12/1 7:45   2.10        13085  United Kingdom  2009-12-01   \n",
       "4        24  2009/12/1 7:45   1.25        13085  United Kingdom  2009-12-01   \n",
       "\n",
       "   cancel_invoice    GMV first_purchase  cohort_index  cohort_month  \\\n",
       "0           False   83.4     2009-12-01             0        200912   \n",
       "1           False   81.0     2009-12-01             0        200912   \n",
       "2           False   81.0     2009-12-01             0        200912   \n",
       "3           False  100.8     2009-12-01             0        200912   \n",
       "4           False   30.0     2009-12-01             0        200912   \n",
       "\n",
       "   invoice_year  invoice_month  \n",
       "0          2009             12  \n",
       "1          2009             12  \n",
       "2          2009             12  \n",
       "3          2009             12  \n",
       "4          2009             12  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1c318da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>country_group</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>GMV</th>\n",
       "      <th>trans_gap</th>\n",
       "      <th>duration</th>\n",
       "      <th>last_gap</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>12</td>\n",
       "      <td>77556.46</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>7</td>\n",
       "      <td>5408.50</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12348</td>\n",
       "      <td>Others</td>\n",
       "      <td>5</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12349</td>\n",
       "      <td>Others</td>\n",
       "      <td>4</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12350</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Customer ID country_group  Invoice       GMV  trans_gap  \\\n",
       "0           0        12346            UK       12  77556.46   3.250000   \n",
       "1           1        12347        Others        7   5408.50   2.000000   \n",
       "2           2        12348        Others        5   2019.40   3.000000   \n",
       "3           3        12349        Others        4   4428.69   6.333333   \n",
       "4           4        12350        Others        1    334.40        NaN   \n",
       "\n",
       "   duration  last_gap  churn  \n",
       "0        13        11      1  \n",
       "1        12         2      0  \n",
       "2        12         3      0  \n",
       "3        23         1      0  \n",
       "4         0        10      1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这个表是一个customer的汇总表，汇总内容包括每个客户的全部invouce，全部gmv,\n",
    "# 首次购买时间到最后一次购买时间差duration\n",
    "# 最后一次购买时间到观察时间差last_gap\n",
    "customer.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbde411b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort_month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200912</td>\n",
       "      <td>0.371728</td>\n",
       "      <td>0.353927</td>\n",
       "      <td>0.451309</td>\n",
       "      <td>0.406283</td>\n",
       "      <td>0.378010</td>\n",
       "      <td>0.405236</td>\n",
       "      <td>0.367539</td>\n",
       "      <td>0.352880</td>\n",
       "      <td>0.390576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.258639</td>\n",
       "      <td>0.317277</td>\n",
       "      <td>0.274346</td>\n",
       "      <td>0.324607</td>\n",
       "      <td>0.301571</td>\n",
       "      <td>0.276440</td>\n",
       "      <td>0.273298</td>\n",
       "      <td>0.338220</td>\n",
       "      <td>0.322513</td>\n",
       "      <td>0.433508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201001</td>\n",
       "      <td>0.213699</td>\n",
       "      <td>0.317808</td>\n",
       "      <td>0.317808</td>\n",
       "      <td>0.273973</td>\n",
       "      <td>0.304110</td>\n",
       "      <td>0.260274</td>\n",
       "      <td>0.232877</td>\n",
       "      <td>0.282192</td>\n",
       "      <td>0.336986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189041</td>\n",
       "      <td>0.150685</td>\n",
       "      <td>0.232877</td>\n",
       "      <td>0.186301</td>\n",
       "      <td>0.183562</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>0.194521</td>\n",
       "      <td>0.232877</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201002</td>\n",
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       "      <td>0.290503</td>\n",
       "      <td>0.248603</td>\n",
       "      <td>0.201117</td>\n",
       "      <td>0.201117</td>\n",
       "      <td>0.276536</td>\n",
       "      <td>0.256983</td>\n",
       "      <td>0.282123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122905</td>\n",
       "      <td>0.203911</td>\n",
       "      <td>0.164804</td>\n",
       "      <td>0.164804</td>\n",
       "      <td>0.142458</td>\n",
       "      <td>0.229050</td>\n",
       "      <td>0.237430</td>\n",
       "      <td>0.167598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201003</td>\n",
       "      <td>0.181176</td>\n",
       "      <td>0.230588</td>\n",
       "      <td>0.237647</td>\n",
       "      <td>0.218824</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.296471</td>\n",
       "      <td>0.272941</td>\n",
       "      <td>0.110588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190588</td>\n",
       "      <td>0.167059</td>\n",
       "      <td>0.169412</td>\n",
       "      <td>0.148235</td>\n",
       "      <td>0.169412</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.209412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201004</td>\n",
       "      <td>0.185053</td>\n",
       "      <td>0.185053</td>\n",
       "      <td>0.160142</td>\n",
       "      <td>0.177936</td>\n",
       "      <td>0.224199</td>\n",
       "      <td>0.274021</td>\n",
       "      <td>0.256228</td>\n",
       "      <td>0.106762</td>\n",
       "      <td>0.106762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153025</td>\n",
       "      <td>0.153025</td>\n",
       "      <td>0.142349</td>\n",
       "      <td>0.145907</td>\n",
       "      <td>0.174377</td>\n",
       "      <td>0.213523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cohort_month         1         2         3         4         5         6  \\\n",
       "0        200912  0.371728  0.353927  0.451309  0.406283  0.378010  0.405236   \n",
       "1        201001  0.213699  0.317808  0.317808  0.273973  0.304110  0.260274   \n",
       "2        201002  0.237430  0.223464  0.290503  0.248603  0.201117  0.201117   \n",
       "3        201003  0.181176  0.230588  0.237647  0.218824  0.200000  0.240000   \n",
       "4        201004  0.185053  0.185053  0.160142  0.177936  0.224199  0.274021   \n",
       "\n",
       "          7         8         9  ...        14        15        16        17  \\\n",
       "0  0.367539  0.352880  0.390576  ...  0.258639  0.317277  0.274346  0.324607   \n",
       "1  0.232877  0.282192  0.336986  ...  0.189041  0.150685  0.232877  0.186301   \n",
       "2  0.276536  0.256983  0.282123  ...  0.122905  0.203911  0.164804  0.164804   \n",
       "3  0.296471  0.272941  0.110588  ...  0.190588  0.167059  0.169412  0.148235   \n",
       "4  0.256228  0.106762  0.106762  ...  0.153025  0.153025  0.142349  0.145907   \n",
       "\n",
       "         18        19        20        21        22        23  \n",
       "0  0.301571  0.276440  0.273298  0.338220  0.322513  0.433508  \n",
       "1  0.183562  0.191781  0.246575  0.194521  0.232877       NaN  \n",
       "2  0.142458  0.229050  0.237430  0.167598       NaN       NaN  \n",
       "3  0.169412  0.200000  0.209412       NaN       NaN       NaN  \n",
       "4  0.174377  0.213523       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rr.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d8111b",
   "metadata": {},
   "source": [
    "# ARPU\n",
    "\n",
    "$$\n",
    "    ARPU = Avg. Transactions\\space Values * Avg. Transaction\\space Frequency * Profit\\space Margin\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ccb1f62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assume perfit margin = 10%\n",
    "margin = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "553d5b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475.874199187778\n"
     ]
    }
   ],
   "source": [
    "# Avg. GMV per transaction\n",
    "tran_GMV = data.groupby([\"Invoice\"])[\"GMV\"].sum().mean()\n",
    "print(tran_GMV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d14ac3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avg. Transaction Frequency per customer per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c44a1023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4488652283552816\n"
     ]
    }
   ],
   "source": [
    "# 用户有购买行为的月份的购买频次\n",
    "tran_freq_1 = data.groupby([\"cohort_index\",\"Customer ID\"])[\"Invoice\"].nunique().mean()\n",
    "print(tran_freq_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2605dbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7846597385781553\n"
     ]
    }
   ],
   "source": [
    "# 用户全生命周期（从第一次购买到最后一次购买）所有月份的购买频次\n",
    "tran_freq_2 = (customer[\"Invoice\"]/(customer[\"duration\"]+1)).mean()\n",
    "print(tran_freq_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1968ff3a",
   "metadata": {},
   "source": [
    "# CLV - strategy\n",
    "$$\n",
    "    CLV per \\space month = ARPU / (1\\space -\\space Monthly\\space Retention\\space Rate)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "edd27c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.94775802745868"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 策略一：希望对于流失客户进行召回，这是我们需要的的频次是所有有购买行为的频次\n",
    "arpu_1 = tran_GMV*tran_freq_1*margin\n",
    "arpu_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b96a8345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
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       "      <th>22</th>\n",
       "      <th>23</th>\n",
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       "    <tr>\n",
       "      <th>cohort_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200912</th>\n",
       "      <td>0.628272</td>\n",
       "      <td>0.646073</td>\n",
       "      <td>0.548691</td>\n",
       "      <td>0.593717</td>\n",
       "      <td>0.621990</td>\n",
       "      <td>0.594764</td>\n",
       "      <td>0.632461</td>\n",
       "      <td>0.647120</td>\n",
       "      <td>0.609424</td>\n",
       "      <td>0.552880</td>\n",
       "      <td>...</td>\n",
       "      <td>0.741361</td>\n",
       "      <td>0.682723</td>\n",
       "      <td>0.725654</td>\n",
       "      <td>0.675393</td>\n",
       "      <td>0.698429</td>\n",
       "      <td>0.723560</td>\n",
       "      <td>0.726702</td>\n",
       "      <td>0.661780</td>\n",
       "      <td>0.677487</td>\n",
       "      <td>0.566492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201001</th>\n",
       "      <td>0.786301</td>\n",
       "      <td>0.682192</td>\n",
       "      <td>0.682192</td>\n",
       "      <td>0.726027</td>\n",
       "      <td>0.695890</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>0.767123</td>\n",
       "      <td>0.717808</td>\n",
       "      <td>0.663014</td>\n",
       "      <td>0.679452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.810959</td>\n",
       "      <td>0.849315</td>\n",
       "      <td>0.767123</td>\n",
       "      <td>0.813699</td>\n",
       "      <td>0.816438</td>\n",
       "      <td>0.808219</td>\n",
       "      <td>0.753425</td>\n",
       "      <td>0.805479</td>\n",
       "      <td>0.767123</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201002</th>\n",
       "      <td>0.762570</td>\n",
       "      <td>0.776536</td>\n",
       "      <td>0.709497</td>\n",
       "      <td>0.751397</td>\n",
       "      <td>0.798883</td>\n",
       "      <td>0.798883</td>\n",
       "      <td>0.723464</td>\n",
       "      <td>0.743017</td>\n",
       "      <td>0.717877</td>\n",
       "      <td>0.885475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.877095</td>\n",
       "      <td>0.796089</td>\n",
       "      <td>0.835196</td>\n",
       "      <td>0.835196</td>\n",
       "      <td>0.857542</td>\n",
       "      <td>0.770950</td>\n",
       "      <td>0.762570</td>\n",
       "      <td>0.832402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201003</th>\n",
       "      <td>0.818824</td>\n",
       "      <td>0.769412</td>\n",
       "      <td>0.762353</td>\n",
       "      <td>0.781176</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.703529</td>\n",
       "      <td>0.727059</td>\n",
       "      <td>0.889412</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.809412</td>\n",
       "      <td>0.832941</td>\n",
       "      <td>0.830588</td>\n",
       "      <td>0.851765</td>\n",
       "      <td>0.830588</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.790588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201004</th>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.839858</td>\n",
       "      <td>0.822064</td>\n",
       "      <td>0.775801</td>\n",
       "      <td>0.725979</td>\n",
       "      <td>0.743772</td>\n",
       "      <td>0.893238</td>\n",
       "      <td>0.893238</td>\n",
       "      <td>0.925267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846975</td>\n",
       "      <td>0.846975</td>\n",
       "      <td>0.857651</td>\n",
       "      <td>0.854093</td>\n",
       "      <td>0.825623</td>\n",
       "      <td>0.786477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201005</th>\n",
       "      <td>0.844622</td>\n",
       "      <td>0.832669</td>\n",
       "      <td>0.820717</td>\n",
       "      <td>0.816733</td>\n",
       "      <td>0.741036</td>\n",
       "      <td>0.792829</td>\n",
       "      <td>0.872510</td>\n",
       "      <td>0.940239</td>\n",
       "      <td>0.916335</td>\n",
       "      <td>0.884462</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900398</td>\n",
       "      <td>0.876494</td>\n",
       "      <td>0.860558</td>\n",
       "      <td>0.836653</td>\n",
       "      <td>0.844622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201006</th>\n",
       "      <td>0.820611</td>\n",
       "      <td>0.809160</td>\n",
       "      <td>0.797710</td>\n",
       "      <td>0.770992</td>\n",
       "      <td>0.713740</td>\n",
       "      <td>0.874046</td>\n",
       "      <td>0.908397</td>\n",
       "      <td>0.916031</td>\n",
       "      <td>0.881679</td>\n",
       "      <td>0.893130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889313</td>\n",
       "      <td>0.877863</td>\n",
       "      <td>0.866412</td>\n",
       "      <td>0.793893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201007</th>\n",
       "      <td>0.841530</td>\n",
       "      <td>0.814208</td>\n",
       "      <td>0.715847</td>\n",
       "      <td>0.715847</td>\n",
       "      <td>0.857923</td>\n",
       "      <td>0.890710</td>\n",
       "      <td>0.852459</td>\n",
       "      <td>0.857923</td>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.863388</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808743</td>\n",
       "      <td>0.825137</td>\n",
       "      <td>0.765027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201008</th>\n",
       "      <td>0.791139</td>\n",
       "      <td>0.696203</td>\n",
       "      <td>0.677215</td>\n",
       "      <td>0.822785</td>\n",
       "      <td>0.879747</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.873418</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.867089</td>\n",
       "      <td>0.867089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.822785</td>\n",
       "      <td>0.797468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201009</th>\n",
       "      <td>0.765690</td>\n",
       "      <td>0.761506</td>\n",
       "      <td>0.874477</td>\n",
       "      <td>0.907950</td>\n",
       "      <td>0.899582</td>\n",
       "      <td>0.861925</td>\n",
       "      <td>0.899582</td>\n",
       "      <td>0.874477</td>\n",
       "      <td>0.866109</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201010</th>\n",
       "      <td>0.743935</td>\n",
       "      <td>0.851752</td>\n",
       "      <td>0.873315</td>\n",
       "      <td>0.908356</td>\n",
       "      <td>0.916442</td>\n",
       "      <td>0.867925</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.892183</td>\n",
       "      <td>0.905660</td>\n",
       "      <td>0.892183</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201011</th>\n",
       "      <td>0.821875</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.903125</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.865625</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.909375</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201012</th>\n",
       "      <td>0.907895</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.907895</td>\n",
       "      <td>0.881579</td>\n",
       "      <td>0.934211</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.907895</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.907895</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201101</th>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.802817</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.845070</td>\n",
       "      <td>0.845070</td>\n",
       "      <td>0.873239</td>\n",
       "      <td>0.887324</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201102</th>\n",
       "      <td>0.837398</td>\n",
       "      <td>0.845528</td>\n",
       "      <td>0.813008</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.845528</td>\n",
       "      <td>0.845528</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201103</th>\n",
       "      <td>0.814607</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.803371</td>\n",
       "      <td>0.775281</td>\n",
       "      <td>0.848315</td>\n",
       "      <td>0.792135</td>\n",
       "      <td>0.797753</td>\n",
       "      <td>0.758427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201104</th>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.819048</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.819048</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201105</th>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.787037</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201106</th>\n",
       "      <td>0.768519</td>\n",
       "      <td>0.787037</td>\n",
       "      <td>0.731481</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>0.712963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201107</th>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.696078</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.656863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201108</th>\n",
       "      <td>0.726415</td>\n",
       "      <td>0.688679</td>\n",
       "      <td>0.735849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201109</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.625668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201110</th>\n",
       "      <td>0.678733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1         2         3         4         5         6  \\\n",
       "cohort_month                                                               \n",
       "200912        0.628272  0.646073  0.548691  0.593717  0.621990  0.594764   \n",
       "201001        0.786301  0.682192  0.682192  0.726027  0.695890  0.739726   \n",
       "201002        0.762570  0.776536  0.709497  0.751397  0.798883  0.798883   \n",
       "201003        0.818824  0.769412  0.762353  0.781176  0.800000  0.760000   \n",
       "201004        0.814947  0.814947  0.839858  0.822064  0.775801  0.725979   \n",
       "201005        0.844622  0.832669  0.820717  0.816733  0.741036  0.792829   \n",
       "201006        0.820611  0.809160  0.797710  0.770992  0.713740  0.874046   \n",
       "201007        0.841530  0.814208  0.715847  0.715847  0.857923  0.890710   \n",
       "201008        0.791139  0.696203  0.677215  0.822785  0.879747  0.898734   \n",
       "201009        0.765690  0.761506  0.874477  0.907950  0.899582  0.861925   \n",
       "201010        0.743935  0.851752  0.873315  0.908356  0.916442  0.867925   \n",
       "201011        0.821875  0.906250  0.903125  0.921875  0.912500  0.865625   \n",
       "201012        0.907895  0.947368  0.907895  0.881579  0.934211  0.947368   \n",
       "201101        0.830986  0.788732  0.802817  0.788732  0.845070  0.845070   \n",
       "201102        0.837398  0.845528  0.813008  0.780488  0.845528  0.845528   \n",
       "201103        0.814607  0.780899  0.803371  0.775281  0.848315  0.792135   \n",
       "201104        0.742857  0.800000  0.800000  0.819048  0.761905  0.819048   \n",
       "201105        0.759259  0.750000  0.833333  0.777778  0.787037  0.740741   \n",
       "201106        0.768519  0.787037  0.731481  0.796296  0.712963       NaN   \n",
       "201107        0.784314  0.696078  0.725490  0.656863       NaN       NaN   \n",
       "201108        0.726415  0.688679  0.735849       NaN       NaN       NaN   \n",
       "201109        0.727273  0.625668       NaN       NaN       NaN       NaN   \n",
       "201110        0.678733       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "                     7         8         9        10  ...        14        15  \\\n",
       "cohort_month                                          ...                       \n",
       "200912        0.632461  0.647120  0.609424  0.552880  ...  0.741361  0.682723   \n",
       "201001        0.767123  0.717808  0.663014  0.679452  ...  0.810959  0.849315   \n",
       "201002        0.723464  0.743017  0.717877  0.885475  ...  0.877095  0.796089   \n",
       "201003        0.703529  0.727059  0.889412  0.882353  ...  0.809412  0.832941   \n",
       "201004        0.743772  0.893238  0.893238  0.925267  ...  0.846975  0.846975   \n",
       "201005        0.872510  0.940239  0.916335  0.884462  ...  0.900398  0.876494   \n",
       "201006        0.908397  0.916031  0.881679  0.893130  ...  0.889313  0.877863   \n",
       "201007        0.852459  0.857923  0.885246  0.863388  ...  0.808743  0.825137   \n",
       "201008        0.873418  0.860759  0.867089  0.867089  ...  0.822785  0.797468   \n",
       "201009        0.899582  0.874477  0.866109  0.882845  ...  0.836820       NaN   \n",
       "201010        0.857143  0.892183  0.905660  0.892183  ...       NaN       NaN   \n",
       "201011        0.900000  0.912500  0.909375  0.887500  ...       NaN       NaN   \n",
       "201012        0.907895  0.947368  0.973684  0.907895  ...       NaN       NaN   \n",
       "201101        0.873239  0.887324  0.788732  0.746479  ...       NaN       NaN   \n",
       "201102        0.853659  0.853659  0.821138       NaN  ...       NaN       NaN   \n",
       "201103        0.797753  0.758427       NaN       NaN  ...       NaN       NaN   \n",
       "201104        0.742857       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201105             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201106             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201107             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201108             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201109             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "201110             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "\n",
       "                    16        17        18        19        20        21  \\\n",
       "cohort_month                                                               \n",
       "200912        0.725654  0.675393  0.698429  0.723560  0.726702  0.661780   \n",
       "201001        0.767123  0.813699  0.816438  0.808219  0.753425  0.805479   \n",
       "201002        0.835196  0.835196  0.857542  0.770950  0.762570  0.832402   \n",
       "201003        0.830588  0.851765  0.830588  0.800000  0.790588       NaN   \n",
       "201004        0.857651  0.854093  0.825623  0.786477       NaN       NaN   \n",
       "201005        0.860558  0.836653  0.844622       NaN       NaN       NaN   \n",
       "201006        0.866412  0.793893       NaN       NaN       NaN       NaN   \n",
       "201007        0.765027       NaN       NaN       NaN       NaN       NaN   \n",
       "201008             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201009             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201010             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201011             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201012             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201101             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201102             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201103             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201104             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201105             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201106             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201107             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201108             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201109             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "201110             NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "                    22        23  \n",
       "cohort_month                      \n",
       "200912        0.677487  0.566492  \n",
       "201001        0.767123       NaN  \n",
       "201002             NaN       NaN  \n",
       "201003             NaN       NaN  \n",
       "201004             NaN       NaN  \n",
       "201005             NaN       NaN  \n",
       "201006             NaN       NaN  \n",
       "201007             NaN       NaN  \n",
       "201008             NaN       NaN  \n",
       "201009             NaN       NaN  \n",
       "201010             NaN       NaN  \n",
       "201011             NaN       NaN  \n",
       "201012             NaN       NaN  \n",
       "201101             NaN       NaN  \n",
       "201102             NaN       NaN  \n",
       "201103             NaN       NaN  \n",
       "201104             NaN       NaN  \n",
       "201105             NaN       NaN  \n",
       "201106             NaN       NaN  \n",
       "201107             NaN       NaN  \n",
       "201108             NaN       NaN  \n",
       "201109             NaN       NaN  \n",
       "201110             NaN       NaN  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这一个公式实际上在计算流失率\n",
    "cohort_churn = 1 - rr.set_index(\"cohort_month\")\n",
    "cohort_churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "86b292eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79376035724626"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## average churn rate\n",
    "churn_rate = cohort_churn.mean(axis=1).mean()\n",
    "churn_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "380da51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.86218377881019"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_1 = arpu_1/churn_rate\n",
    "clv_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea947fa6",
   "metadata": {},
   "source": [
    "# CLV - lifespan\n",
    "$$\n",
    "    CLV = ARPU * Avg. Lifespan\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e789366c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.33993247307709"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 此种情形适用于在获客阶段直接提供“新手大礼包”，\n",
    "# 此时我们要关注的是客户在全生命周期中的消费频次，\n",
    "# 因此我们需要计算的是用户从第一次购买到最后一次购买过程中的全部时间\n",
    "\n",
    "arpu_2 = tran_GMV * tran_freq_2 * margin\n",
    "arpu_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "547330e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assume Avg. Lifespan = 16 months\n",
    "lifeSpan = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a0803610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597.4389195692335"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_2 = arpu_2 * lifeSpan\n",
    "clv_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57bb0e6",
   "metadata": {},
   "source": [
    "# Survival Analysis for Lifespan Estimation\n",
    "1. 可以帮助我们进行lifespan的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3328ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3281</td>\n",
       "      <td>12.640049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2572</td>\n",
       "      <td>3.993002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer ID   duration\n",
       "churn                        \n",
       "0             3281  12.640049\n",
       "1             2572   3.993002"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.groupby('churn') \\\n",
    "        .agg({\"Customer ID\":\"nunique\",\n",
    "              \"duration\":\"mean\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0a965a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1795</td>\n",
       "      <td>0.707521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>226</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195</td>\n",
       "      <td>0.420513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>167</td>\n",
       "      <td>0.670659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152</td>\n",
       "      <td>0.565789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>154</td>\n",
       "      <td>0.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>133</td>\n",
       "      <td>0.593985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>159</td>\n",
       "      <td>0.622642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>133</td>\n",
       "      <td>0.533835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>124</td>\n",
       "      <td>0.669355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>121</td>\n",
       "      <td>0.586777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>143</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>250</td>\n",
       "      <td>0.328000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>198</td>\n",
       "      <td>0.297980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>136</td>\n",
       "      <td>0.345588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>150</td>\n",
       "      <td>0.413333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>138</td>\n",
       "      <td>0.275362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>156</td>\n",
       "      <td>0.179487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>143</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>188</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>224</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>203</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>414</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Customer ID     churn\n",
       "duration                       \n",
       "0                1795  0.707521\n",
       "1                 226  0.500000\n",
       "2                 195  0.420513\n",
       "3                 167  0.670659\n",
       "4                 152  0.565789\n",
       "5                 154  0.590909\n",
       "6                 133  0.593985\n",
       "7                 159  0.622642\n",
       "8                 133  0.533835\n",
       "9                 124  0.669355\n",
       "10                121  0.586777\n",
       "11                143  0.538462\n",
       "12                250  0.328000\n",
       "13                198  0.297980\n",
       "14                136  0.345588\n",
       "15                150  0.413333\n",
       "16                138  0.275362\n",
       "17                156  0.179487\n",
       "18                143  0.153846\n",
       "19                188  0.000000\n",
       "20                224  0.000000\n",
       "21                151  0.000000\n",
       "22                203  0.000000\n",
       "23                414  0.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.groupby('duration') \\\n",
    "        .agg({\"Customer ID\":\"nunique\",\n",
    "              \"churn\":\"mean\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e1eb2ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer.groupby(\"duration\")[\"churn\"].mean() \\\n",
    "        .plot.line(figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8397e53c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lifelines\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "kmf = lifelines.KaplanMeierFitter()\n",
    "kmf.fit(customer[\"duration\"],\n",
    "        customer[\"churn\"])\n",
    "kmf.plot_survival_function()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1fa6d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KM_estimate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.783017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.761213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.744924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.721985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.704091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.684780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.667683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.645874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.629907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.610819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.594235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.575888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.555802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.540194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.526853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.508367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.496420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.487022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.478923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          KM_estimate\n",
       "timeline             \n",
       "0.0          0.783017\n",
       "1.0          0.761213\n",
       "2.0          0.744924\n",
       "3.0          0.721985\n",
       "4.0          0.704091\n",
       "5.0          0.684780\n",
       "6.0          0.667683\n",
       "7.0          0.645874\n",
       "8.0          0.629907\n",
       "9.0          0.610819\n",
       "10.0         0.594235\n",
       "11.0         0.575888\n",
       "12.0         0.555802\n",
       "13.0         0.540194\n",
       "14.0         0.526853\n",
       "15.0         0.508367\n",
       "16.0         0.496420\n",
       "17.0         0.487022\n",
       "18.0         0.478923\n",
       "19.0         0.478923\n",
       "20.0         0.478923\n",
       "21.0         0.478923\n",
       "22.0         0.478923\n",
       "23.0         0.478923"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.survival_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "606e8a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "kmf.median_survival_time_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd929c3d",
   "metadata": {},
   "outputs": [],
   "source": []
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